Time-scale theory, due to its ability to unify the continuous and discrete cases, allows handling intractable non-uniform measurements, such as intermittent received signals. In this work, we address the state estimation problem of a vibration-induced intermittent optical wireless communication (OWC) system by designing a Kalman filter on time scales. First, the algorithm of the time-scale Kalman filter is introduced and a numerical example is given for illustration. Then the studied intermittent OWC system is presented, and experimental data are collected to determine the time scale's form, which has bounded graininess (a.k.a, bounded time jumps). Finally, we design a Kalman filter on the previously defined time scale for the intermittent OWC system and critically analyzed its estimation performance. Moreover, the obtained conclusions are further validated on a reference system. The simulation results corroborate that the time-scale Kalman filtering technique is considerably promising to solve the state estimation problem with non-uniform measurements. This study reveals for the first time the feasibility of applying the time-scale Kalman filter theory to practical applications.
Stability of Kalman Filtering with a Random Measurement Equation: Application to Sensor Scheduling with Intermittent Observations
Guandong University of Technology（广东工业大学）；French-Argentinean International Center for Information and Systems Sciences, National Scientific and Technical Research Council（法阿信息与系统科学国际中心，国家科学技术研究委员会）；Dalian University of Technology（大连理工大学）；ABB Corporate Research Center（ABB企业研究中心）；University of Newcastle（纽卡斯尔大学）
KTH Royal Institute of Technology（KTH皇家理工学院）；University of Cyprus（塞浦路斯大学）；Aalto University（阿尔托大学）；Cyprus University of Technology（塞浦路斯理工大学）
We present a multisensor fusion framework for the onboard real-time navigation of a quadrotor in an indoor environment. The framework integrates sensor readings from an Inertial Measurement Unit (IMU), a camera-based object detection algorithm, and an Ultra-WideBand (UWB) localisation system. Often the sensor readings are not always readily available, leading to inaccurate pose estimation and hence poor navigation performance. To effectively handle and fuse sensor readings, and accurately estimate the pose of the quadrotor for tracking a predefined trajectory, we design a Maximum Correntropy C…
Power Scheduling of Kalman Filtering in Wireless Sensor Networks with Data Packet Drops
School of Automation, Beijing Institute of Technology（北京理工大学自动化学院）；Key Laboratory of Intelligent Control and Decision of Complex Systems（复杂系统智能控制与决策重点实验室）
In wireless positioning systems, non-line-of-sight (NLOS) is a challenging problem. NLOS causes great ranging bias and location error, so NLOS mitigation is essential for high accuracy positioning. In this letter, we propose the Weighted-Least-Squares Robust Kalman Filter (WLS-RKF) for NLOS identification and mitigation. WLS-RKF employs a hypothesis test based on Mahalanobis distance for NLOS identification, and updates the corresponding Kalman filter using the WLS solution. It requires no prior knowledge about NLOS distribution or signal features. We perform simulations and experiments for ul…
KTH Royal Institute of Technology（瑞典皇家理工学院）；Cyprus University of Technology（塞浦路斯理工大学）；University of Cyprus（塞浦路斯大学）；Aalto University（阿尔托大学）
We propose a multisensor fusion framework for onboard real-time navigation of a quadrotor in an indoor environment, by integrating sensor readings from an Inertial Measurement Unit (IMU), a camera-based object detection algorithm, and an Ultra-WideBand (UWB) localization system. The sensor readings from the camera-based object detection algorithm and the UWB localization system arrive intermittently, since the measurements are not readily available. We design a Kalman filter that manages intermittent observations in order to handle and fuse the readings and estimate the pose of the quadrotor f…
System Identification for Continuous-time Linear Dynamical Systems
Princeton University（普林斯顿大学）；Princeton Neuroscience Institute（普林斯顿神经科学研究所）；Columbia University（哥伦比亚大学）；Data Science Institute, Columbia University（哥伦比亚大学数据科学研究所）；New York Genome Center（纽约基因组中心）
Accurate runoff forecasting is crucial for reservoir operators as it allows optimized water management, flood control and hydropower generation. Land surface models in mountainous regions depend on climatic inputs such as precipitation, temperature and solar radiation to model the water and energy dynamics and produce runoff as output. With the rapid development of cheap electronics applied in various systems, such as Wireless Sensor Networks (WSNs), satellite and airborne technologies, the prospect of practically measuring spatial Snow Water Equivalent in a dense temporal scale is increasing.…
This paper explores the fundamental limits of a simple system, inspired by the intermittent Kalman filtering model, where the actuation direction is drawn uniformly from the unit hypersphere. The model allows us to focus on a fundamental tension in the control of underactuated vector systems -- the need to balance the growth of the system in different dimensions. We characterize the stabilizability of $d$-dimensional systems with symmetric gain matrices by providing tight necessary and sufficient conditions that depend on the eigenvalues of the system. The proof technique is slightly different…
Utilizing millimeter-wave (mmWave) frequencies for wireless communication in \emph{mobile} systems is challenging since it requires continuous tracking of the beam direction. Recently, beam tracking techniques based on channel sparsity and/or Kalman filter-based techniques were proposed where the solutions use assumptions regarding the environment and device mobility that may not hold in practical scenarios. In this paper, we explore a machine learning-based approach to track the angle of arrival (AoA) for specific paths in realistic scenarios. In particular, we use a recurrent neural network …
A Kalman Filter-Assisted Data-Predictive SAR ADC With Reduced Switching Energy for Low-Power Applications
School of Integrated Circuits (School of Microelectronics) Northwestern Polytechnical University（西北工业大学集成电路学院（微电子学院））；Suzhou Institute of Nano-Tech Nano-Bionics Chinese Academy of Sciences（中国科学院苏州纳米技术与纳米仿生研究所）
针对超低功耗 ADC 需求，提出卡尔曼滤波器辅助数据预测型 SAR ADC，通过预测 4 个 MSB 实现并行切换，减少开关能量和量化周期，优化切换方案，双模式操作，降低功耗，适用于无线传感器网络。
Nowadays, accurate localization plays an essential role in many fields, like target tracking and path planning. The challenges of indoor localization include inadequate localization accuracy, unreasonable anchor deployment in complex scenarios, lack of stability, and high cost. So the universal positioning technologies cannot meet the real application requirements scarcely. To overcome these shortcomings, a comprehensive Ultra Wide-Band (UWB) based RTLS is presented in this paper. We first introduce the architecture of the real-time localization system, then propose a new wireless clock synchr…
Spatio-Temporal Attention Enhanced Multi-Agent DRL for UAV-Assisted Wireless Networks with Limited Communications
School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-sen University（中山大学智能系统工程学院深圳校区）；Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai)（广东海洋工程科学实验室（珠海））；Department of Equipment Management and Unmanned Aerial Vehicle Engineering, Air Force Engineering University（空军工程大学装备管理与无人机工程系）；School of Computing and Artificial Intelligence, Jiangxi University of Finance and Economics（江西财经大学计算机与人工智能学院）；College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算机与数据科学学院）
